Artificial Intelligence in Finance : How algorithms are taking over trading
Dalgamoni, Kalle (2022)
2022
Tietojenkäsittelytieteiden kandidaattiohjelma - Bachelor's Programme in Computer Sciences
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
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Hyväksymispäivämäärä
2022-06-03
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202205265280
https://urn.fi/URN:NBN:fi:tuni-202205265280
Tiivistelmä
The study objective for this thesis is to investigate how artificial intelligence is being applied in the financial sector and further study algorithmic trading (AT) and its subset high-frequency trading (HFT). The thesis is a literature review, and the research question is "How is artificial intelligence utilized in stock trading?”.
Artificial intelligence is disrupting many industries, including the finance industry. Artificial intelligence (AI) has infiltrated practically every aspect of the finance industry. The main objective for this thesis is to provide basic knowledge on AT and HFT and to further investigate the effects HFT has on the market. The majority of the references used in this thesis are peer reviewed scientific publications with the additions of books and a report.
AT is automated trading which is conducted on computers while utilizing algorithms. HFT is a subset of AT which relies on speed and short term trades. Institutional investors such as hedge funds are the primary users for AT. As of the year 2020 AT has been responsible for an estimate of 85 percent of all the executed trades, which solidifies its significance in the financial sector. The algorithms developed for trading utilize different artificial intelligence technologies such as machine learning and deep learning. AT and HFT need highly developed and complex algorithms, and HFT further needs fast and low-latency connections. AT and HFT have influenced the stock market heavily and the study on the effects has increased in the recent years.
This thesis shows that the use of HFT has become more common. The effects on the market by HFT can be seen as primarily positive, with being HFT related to improved liquidity, i.e., volume of demand and supply, and price efficiency. The negative effects of HFT are related to increased volatility, i.e., the standard deviation of returns. Due to HFT the investments on network infrastructure have increased in the United Stated. Future research topics regarding AT and HFT may focus on many areas of computer science, such as the technological development of networks, computing power, or algorithms.
Artificial intelligence is disrupting many industries, including the finance industry. Artificial intelligence (AI) has infiltrated practically every aspect of the finance industry. The main objective for this thesis is to provide basic knowledge on AT and HFT and to further investigate the effects HFT has on the market. The majority of the references used in this thesis are peer reviewed scientific publications with the additions of books and a report.
AT is automated trading which is conducted on computers while utilizing algorithms. HFT is a subset of AT which relies on speed and short term trades. Institutional investors such as hedge funds are the primary users for AT. As of the year 2020 AT has been responsible for an estimate of 85 percent of all the executed trades, which solidifies its significance in the financial sector. The algorithms developed for trading utilize different artificial intelligence technologies such as machine learning and deep learning. AT and HFT need highly developed and complex algorithms, and HFT further needs fast and low-latency connections. AT and HFT have influenced the stock market heavily and the study on the effects has increased in the recent years.
This thesis shows that the use of HFT has become more common. The effects on the market by HFT can be seen as primarily positive, with being HFT related to improved liquidity, i.e., volume of demand and supply, and price efficiency. The negative effects of HFT are related to increased volatility, i.e., the standard deviation of returns. Due to HFT the investments on network infrastructure have increased in the United Stated. Future research topics regarding AT and HFT may focus on many areas of computer science, such as the technological development of networks, computing power, or algorithms.